Ethics of AI: A Systematic Literature Review of Principles and Challenges

• URL: http://arxiv.org/abs/2109.07906v1
• Date: Sun, 12 Sep 2021 15:33:43 GMT
• Title: Ethics of AI: A Systematic Literature Review of Principles and Challenges
• Authors: Arif Ali Khan, Sher Badshah, Peng Liang, Bilal Khan, Muhammad Waseem, Mahmood Niazi, Muhammad Azeem Akbar
• Abstract summary: Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI.
• Score: 3.7129018407842445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.

Related papers

• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Survey on AI Ethics: A Socio-technical Perspective [0.9374652839580183]
  Ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. This work unifies the current and future ethical concerns of deploying AI into society.
  arXiv  Detail & Related papers  (2023-11-28T21:00:56Z)
• Unpacking the Ethical Value Alignment in Big Models [46.560886177083084]
  This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
  arXiv  Detail & Related papers  (2023-10-26T16:45:40Z)
• Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions [1.864621482724548]
  We develop a taxonomy of 21 normative ethical principles which can be operationalised in AI. We envision this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in reasoning capacities of responsible AI systems.
  arXiv  Detail & Related papers  (2022-08-12T08:48:16Z)
• AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers [8.82540441326446]
  Transparency, accountability, and privacy are the most critical AI ethics principles. Lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are the most common AI ethics challenges.
  arXiv  Detail & Related papers  (2022-06-30T17:24:29Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis [12.031113181911627]
  Artificial Intelligence (AI) is transforming our daily life with several applications in healthcare, space exploration, banking and finance. These rapid progresses in AI have brought increasing attention to the potential impacts of AI technologies on society. Several ethical principles have been released by governments, national and international organisations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI.
  arXiv  Detail & Related papers  (2022-05-12T22:41:08Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• AI virtues -- The missing link in putting AI ethics into practice [0.0]
  The paper defines four basic AI virtues, namely justice, honesty, responsibility and care. It defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues.
  arXiv  Detail & Related papers  (2020-11-25T14:14:47Z)
• Case Study: Deontological Ethics in NLP [119.53038547411062]
  We study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems.
  arXiv  Detail & Related papers  (2020-10-09T16:04:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.